Calibration-Enhanced 16-Channel On-Chip Seizure Classifier using Gated Recurrent Network

The development of implantable on-chip machine learning (ML) classifiers has opened new possibilities for treating neurological disorders through closed-loop responsive neurostimulation. However, implementing ML algorithms in hardware requires optimizing area and power efficiency while maintaining low processing latency. Additionally, non-idealities in the analog front-end can significantly affect the classifier’s real-time performance. This paper explores the influence of these effects and proposes a gain correction technique to be implemented during the training phase, thereby enhancing the system’s robustness. A 16-channel time-division multiplexed (TDM) digital backend system was designed in 65nm CMOS technology. The designed System on Chip (SoC) relies on three spectral features from distinct physiological frequency bands, extracted via a FIR filter, and was tested on EEG data from the CHB-MIT scalp EEG database. The proposed implementation across process corners achieved a sensitivity of 95.4%, specificity of 93.3%, accuracy of 94.4% and the energy efficiency of the digital system was 24.126 µJ/classification at 0.9V.